Parametric Design Research Articles

Research on optimization of the vehicle handling stability considering flexibility of the front subframe

The elastic deformation of the front subframe during vehicle traveling affects the vehicle handling stability. The static equilibrium equations of the suspension and subframe system and the kinematics equations of the three-degree-of-freedom vehicle are established to illustrate the principle of the influence of the flexible front subframe on the handling stability. The front subframe is flexibly processed to establish a rigid-flexible coupled vehicle model. Simulations of the suspension kinematic and compliance alongside sine swept steering simulation of the vehicle are performed. The front subframe bushing stiffness, which has a greater impact on the handling stability, is selected as a design variable through parametric test design, and the NSGA-II algorithm is used for multi-objective optimization with the vehicle handling stability evaluation index as the optimization target. The simulation results show that the flexible front subframe will make the bushing force change to affect the suspension kinematic and compliance characteristics and then affect the vehicle handling stability, the gain of vehicle yaw rate and the delay time of lateral acceleration decrease, while the gain of roll angle increases. The optimization results show that the optimized vehicle exhibits improvements in the yaw rate gain, delay time of lateral acceleration, and roll angle gain.

The new bridge “Lahnsteg Nauheim”, Wetzlar: slender, elegant, parametrically designed

AbstractThis paper outlines the planning of a bridge in central Germany. Scheduled for construction in mid‐2024, this new bridge is a replacement for a century‐old pedestrian bridge crossing the Lahn river.The proposed bridge will be an airtight‐welded arch bridge, employing steel cross‐sections in both the arch and tension member. Designed as a single‐span, the arches extend 48.0m over the Lahn. The two arches have a flat rise of 4.65m, supporting the roadway through 9 inclined flat steel hangers.This paper delves into the structural engineering aspects, exploring challenges inherent in the design process. An examination of the parametric design process, facilitated through 3‐D modeling in Rhino® and Grasshopper®, will be presented, along with insights into their interface with the finite element modeling software, RFEM®. This process enables an engineering approach to form‐finding and structure optimisation through iterative design checks for various elements and stages of the structural analysis.

Multi-objective optimization of high Mach waverider based on small-sample surrogate model

Advancements have been achieved in the optimization of waverider designs with the aid of machine learning to expedite the design process. However, these approaches are hampered by the need for extensive sample sizes and susceptibility to becoming ensnared in local optima. This study undertakes a parametric design based on the wedge-derived, power-law-shaped waverider, increasing configuration diversity and creating a dataset with limited samples by calculating waverider geometry and aerodynamic parameters. At a Mach number of 10, a multi-objective optimization design is implemented using the Young's double-slit experiment-least squares support vector regression (YDSE-LSSVR) surrogate model in conjunction with improved congestion distance multi-objective particle swarm optimization algorithm, focusing on maximizing the lift-to-drag ratio and volumetric efficiency as much as possible. The results indicated that, under conditions of limited samples, the YDSE-LSSVR model outperforms standard models such as support vector regression, LSSVR, Kriging, and Polynomial Chaos Expansions-Kriging regarding prediction accuracy. The Pareto solutions for both concave and convex waveriders, obtained through multi-objective optimization, improve the lift-to-drag ratio by 17.36% and 21.70%, respectively, and increase the volumetric efficiency by 88.89% and 105.56%, in comparison to baseline configurations. In addition, the research examines the impact of various design parameters on the Pareto solutions. Finally, the study applies the K-means method to conduct a cluster analysis of the Pareto solutions, generating three-dimensional waverider configurations based on distinguished solutions from different clusters.

Grid-connected virtual synchronous generator transient suppression based on proportional differential control and frequency low-pass filtering

Abstract The virtual synchronous generator (VSG) technology has a wide range of applications in various distributed generation units. However, sudden changes in power command at grid connection can lead to severe oscillations in the output power and frequency of virtual synchronous generators. In addition, existing oscillation suppression methods are either based on complex parameter designs or change the original inertial support characteristics and power frequency drop characteristics. In this paper, a new transient suppression method is proposed, i.e., a proportional differential controller is added behind the power error and an additional low-pass filter is placed behind the output frequency. Finally, by establishing a simulation platform, it is verified that the proposed scheme can greatly reduce the overshoot, regulation time and frequency offset of the active power.

Analysis and Controller Design for Parameter Varying T-S Fuzzy Systems with Markov Jump

In this paper, we investigate a novel T-S fuzzy parameter varying system with Markov jump, in which parameters depend not only on a Markov chain but also on linear parameter varying elements that take values in convex polytopic sets. Stable conditions and the gain-scheduling controller design method for this system are obtained. Applying Lyapunov function depending on the operation mode and full block S-procedure lemma, we obtain stochastic stabilization conditions. We find that this novel system has two distinct advantages. On the one hand, it inherits the advantages of traditional T-S fuzzy systems in handling nonlinear objects under the frame of T-S fuzzy systems; on the other, it obtains the advantages of dealing with time-varying characteristics from the point of linear parameter varying (LPV) systems. Finally, the theory results are illustrated via numerical simulation.

Optimization of BIPV Utilization with Parametric Approach: Case Study on Nearly Zero Carbon Building Studio Design in Medan, Indonesia

Buildings significantly consume fossil energy, leading to substantial carbon emissions contributing to global warming and rising sea levels. These environmental impacts pose serious challenges, necessitating a shift towards sustainable building designs. By integrating clean energy solutions into building architecture, we can reduce the environmental burden of emissions. This approach mitigates adverse effects on living organisms and ecosystems and promotes long-term sustainability and resilience in urban development. Building-Integrated Photovoltaic (BIPV) systems represent one such approach for substituting fossil energy. However, implementing BIPV effectively necessitates comprehensive analysis considering numerous parameters and variables. This paper introduces building design strategies employing the BIPV approach. The decision-making process in designing alternatives utilizes parametric methods, recognized for their ability to yield optimization results for various variables through the development of design algorithms. The study focuses on the design of a studio building at the University of North Sumatra. Comparative analysis of the results obtained through parametric design aims to identify the most favorable design outcomes. The research includes developing a parameterized design algorithm, workflow, and assessment results for selecting optimal design alternatives. The results indicate that the placement of PV panels on the side area (PVOS) has more potential than placement on the top area (PVOT). Despite the PVOT area being observed to be 0.57% smaller than PVOS, PVOS shows a significant increase in total Incident Radiation (IR), average IR, and total Energy Generated by 137.14%, 128.40%, and 132.97%, respectively.

Design of nonlinear locking mechanism for shape memory alloy archwire of miniature orthodontic device

The locking mechanism between bracket and shape memory alloy (SMA) archwire in the newly developed domestic orthodontic device is the key to controlling the precise alignment of the teeth. To meet the demand of locking force in clinical treatment, the tightening torque angle of the locking bolt and the required torque magnitude need to be precisely designed. For this purpose, a design study of the locking mechanism is carried out to analyze the correspondence between the tightening torque angle and the locking force and to determine the effective torque value, which involves complex coupling of contact, material and geometric nonlinear characteristics. Firstly, a simulation analysis based on parametric orthogonal experimental design is carried out to determine the SMA hyperelastic material parameters for the experimental data of SMA archwire with three-point bending. Secondly, a two-stage fine finite-element simulation model for bolt tightening and archwire pulling is established, and the nonlinear analysis is converged through the optimization of key contact parameters. Finally, multiple sets of calibration experiments are carried out for three tightening torsion angles. The comparison results between the design analysis and the calibration experiments show that the deviation between the design analysis and the calibration mean value of the locking force in each case is within 10%, and the design analysis method is valid and reliable. The final tightening torque angle for clinical application is determined to be 10° and the rated torque is 2.8 N∙mm. The key data obtained can be used in the design of clinical protocols and subsequent mechanical optimization of novel orthodontic devices, and the research methodology can provide a valuable reference for force analysis of medical devices containing SMA materials.

Development of a personalized mask design method using three-dimensional scan data

Purpose This study aims to develop a novel design method to make personalized masks for the effective prevention of pandemic respiratory infectious disease.Design/methodology/approachThe changes in facial shape during speaking were analyzed using a three-dimensional (3D) scanning technique. In total, 13 anthropometric items were measured, and mask patterns were generated using a parametric pattern design method. Three sizing methods were proposed to reflect not only static but also dynamic body dimensions on the mask patterns.FindingsA significant increase or decrease was observed in 10 out of 13 measurement items. Based on this, four items were selected to be used in the mask pattern design. The nose and cheek areas of a mask were fixed to protect the respiratory tract against viruses. The lower jaw area was deformed to improve the fit.Social implicationsThis study is expected to provide fundamental data to understand the changes in facial shape during movement. In addition, it is expected that the development of individualized personal protective equipment with movement adaptability will facilitate an effective response to various pandemic respiratory diseases.Originality/valueIn order to develop a personal protective equipment (PPE) that has a good fit and can protect against pandemic respiratory infectious diseases, morphological analysis was attempted using 3D facial data. It would be possible to design various products and equipment to be worn on the face by using the method proposed in this study.

Fracture mechanics of bi-material lattice metamaterials

The advent of additive manufacturing technology empowers precise control of multi-material components or specific defects in lightweight lattice metamaterials, however, fracture mechanics and toughening design strategies in such metamaterials remain enigmatic. By incorporating theoretical analysis, numerical simulation, and experimental investigation, our study reveals that stretch-bend synergistic strut deformations caused by bi-material components or topology defects contribute notably tougher lattice structures surpassing its ideal single-material lattices. A peak fracture energy at a critical modulus ratio was found in a designed bi-material lattice composed of triangular soft struts and hexagonal stiff struts, which originates from the shift of fracture modes at crack tip from strut bending to stretching dominated failure modes as the modulus of soft struts increases, where the compromise in competition between bending-enhanced and stretching-weakened energy dissipations of struts deformations results in the maximized fracture energy. A parametric design protocol was proposed to optimize fracture energy of bi-material lattices through tuning the modulus ratio and relative density. Furthermore, the concept of stretch-bend synergistic toughening can also be applied to make tougher single-material lattices with specific topological defects. Our findings not only provide physical insights into directing crack propagation but also provide quantitative guidance to optimize fracture resistance within low-density tough lattice metamaterials.

Multi-Objective Optimization of a Folding Kinetic Facade System Proposal for Thermal, Daylight, and Energy Performances

Efficient utilization of daylight and energy resources significantly influences the quality of indoor spaces, user comfort, and overall efficiency. This study presents a folding facade proposal through the design alternatives offered by kinetic architecture and parametric design to enhance efficiency. This alternative design method integrates and coordinates the design components simultaneously and makes any intervention easier when compared with traditional design methods. In this context, the method is based on computational models, aiming to find the most efficient design alternative by optimization. The proposed facade design specifically targets an indoor office space within a university. The modular system, integrated into existing windows, facilitates a folding movement. This dynamic feature aims to optimize illumination within the space, effectively controlling daylight without causing disruptions to users. Simultaneously, the design seeks to balance energy consumption and ensure thermal comfort. The results show that it provides a significant improvement over the base case. The proposed kinetic façade system improved indoor thermal comfort by 22.09-31.99% while slightly increasing energy use (2% at most). Although it provided an average of 59% improvement in spatial Daylight Autonomy (sDA), 13.30% - 87.38% was achieved compared to the base case. However, the desired value by LEED was achieved very little in Annual Sunlight Exposure (ASE). In conclusion, the proposed kinetic facade system proves to be a valuable intervention for enhancing the indoor environment of an office space at Dokuz Eylül University

Parametric design methodology for yoke-magnetization in magnetic flux leakage detection systems

Magnetic flux leakage (MFL) testing is a widely employed non-destructive testing (NDT) method, particularly for ferromagnetic materials. This paper proposes a systematic design procedure for the yoke-magnetization component in MFL systems utilizing permanent magnets. The dimensions of the yoke-magnetization are parameterized, and the design methodology is formulated in the form of analytical equations based on fundamental engineering principles and optimization techniques. The proposed design procedure outlines all the required parameters and metrics to establish a yoke-magnetization configuration tailored for a specified application, thereby facilitating scalability. The theoretical framework identifies the optimal operating point that maximizes leakage flux while minimizing the magnetic field amplitude. A genetic algorithm is employed to minimize the dimensions of the permanent magnets while maintaining a sufficient magnetic field strength for specimen magnetization. An experimental setup is constructed to validate the accuracy of the proposed design procedure. The experimental results exhibit consistency with finite element method (FEM) simulations conducted using a case study. Specifically, the findings reveal that the thickness of the magnetic bridge significantly impacts the leakage fluxes at defect sites within the specimen. When the thickness of the magnetic bridge is 10 mm, slightly smaller than the optimal thickness of approximately 11 mm, the leakage flux at the defect site leaks out towards the surrounding air insignificantly. In contrast, a thickness of 15 mm is observed to strongly improve the leakage flux at the defect site.

A Comparative Evaluation of Conveyor Belt Disc Brakes and Drum Brakes: Integrating Structural Topology Optimization and Weight Reduction

Topology optimization is a well known and sophisticated method for designing structures. Through a finite element analysis, this method optimizes the design and material distribution to obtain an ideal strength-to-weight ratio and improved strain-to-weight ratio. This study involves the development of a comprehensive model for a brake using the ANSYS Parametric Design Language. The purpose of the model is to accurately characterize the geometry of the disc or drum. The technique of a complex eigenvalue analysis is used to identify the presence of unstable modes occurring at distinct frequencies, indicating instability. A braking force of 17,492 kN was exerted at a rotational velocity of 55 rad/s for 10 s. The optimization process resulted in significant mass reduction while maintaining structural integrity. In the drum brake, the mass was reduced from 114.01 kg to 104.07 kg, while the disc brake’s mass decreased from 68.81 kg to 56.68 kg.

Novel symmetry corrugate hierarchical honeycomb for superior crashworthiness

Honeycombs are extensively utilized as energy absorber due to their stabilized mechanical properties. Therefore, we propose a novel symmetry-corrugate hierarchical design strategy to construct symmetry-corrugate hierarchical honeycombs (SCHH). Crashworthiness and mechanical properties of SCHH are investigated by experiments and numerical simulation. SCHH exhibits a stable and efficient mechanical property in the compression test. Furthermore, numerical simulations have been conducted to investigate the parametric design of SCHH. The results show the effects of different period coefficients φT and amplitude coefficients φA on deformation modes and crashworthiness. Specifically, as φT increases and φA decreases, the deformation changes from progressive deformation mode (P-mode) to mixed deformation mode (M-mode) to global bending deformation mode (G-mode). P-mode and M-mode have better crashworthiness. The effect of φT on the crashworthiness of SCHH is more significant than φA. In addition, SCHH provides the highest crashworthiness compared with other honeycombs. Specifically, the energy absorption capacity and crushing stability are improved by 53.8 % and 68.5 %, respectively, compared with conventional honeycomb. Eventually, a crashworthiness prediction model of SCHH is developed by machine learning algorithms, and the model is verified to have high accuracy. In this study, the proposed strategy and methods are expected to further improve the crashworthiness of honeycomb.

Co-design studio with different design environments: Analogue design, designing within the design and parametric design

Co-design studio with different design environments: Analogue design, designing within the design and parametric design

A pointwise weighting prediction variance–high-dimensional model representation model-based global optimization approach for ship hull parametric design

Ship hull optimization techniques based on computer-aided design/computational fluid dynamics can effectively enhance the efficiency and stability of ship designs, with significant application prospects. To enhance the potential of ship hull optimization, increasing design variable dimensionality is essential, but can cause a significant increase in hydrodynamic simulations. To reduce simulations required for high-dimensional ship hull optimization, a new surrogate method, pointwise weighting prediction variance–high-dimensional model representation (PWPV-HDMR), which uses pointwise weighting prediction variance (PWPV) to aggregate different a priori assumptions, is developed. Moreover, a differential evolution algorithm is used to identify promising hull design parameters, using the PWPV-HDMR model instead of costly simulation as the fitness function. The proposed approach is tested on the hydrostatic resistance optimization of KRISO Container Ship. The results show that PWPV-HDMR outperforms kriging-HDMR, with a better resistance optimization effect, illustrating the effectiveness of the PWPV-HDMR-based global optimization approach in discovering promising ship hull parameters.

Parametric Design Method of Buildings Based on Convolutional and Deep Neural Networks

The application of AI technology in architectural software has brought various innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them, and analyzes the application of these technologies in architectural image recognition, generation, design optimization and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design. The application of AI technology in architectural software has brought numerous innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them; it also analyzes the application of these technologies in architectural image recognition, generation, design optimization, and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design.

Study on Vibration and Noise of Railway Steel–Concrete Composite Box Girder Bridge Considering Vehicle–Bridge Coupling Effect

A steel–concrete composite beam bridge fully exploits the mechanical advantages of the concrete structure and steel structure, and has the advantages of a fast construction speed and large stiffness. It is of certain research value to explore the application of this bridge type in the field of railway bridges. However, with the rapid development of domestic high-speed railway construction, the problem of vibration and noise radiation of high-speed railway bridges caused by train loads is becoming more and more serious. A steel–concrete composite beam bridge combines the tensile characteristics of steel and the compressive characteristics of concrete perfectly. At the same time, it also has the characteristics of a steel bridge and concrete bridge in terms of vibration and noise radiation. This feature makes the study of the vibration and noise of the bridge type more complicated. Therefore, in this paper, the characteristics of vibration and noise radiation of a high-speed railway steel–concrete composite box girder bridge are studied in detail from two aspects: the theoretical basis and a numerical simulation. The main results obtained are as follows: Relying on the idea of vehicle–rail–bridge coupling dynamics, a structural dynamics analysis model of a steel–concrete combined girder bridge for a high-speed railroad was established, and numerical program simulation of the vibration of the vehicle–rail–bridge coupling system was carried out based on the parametric design language of ANSYS 18.0 and the language of MATLAB R2021a, and the structural vibration results were analyzed in both the time domain and frequency domain. By using different time-step loading for the vehicle–rail–bridge coupling vibration analysis, the computational efficiency can be effectively improved under the condition of guaranteeing the accuracy of the result analysis within 100 Hz. Based on the power flow equilibrium equation, a statistical energy method of calculating the high-frequency noise radiation is theoretically derived. Based on the theoretical basis of the statistical energy method, the high-frequency noise in the structure is numerically simulated in the VAONE 2021 software, and the average contribution of the concrete roof plate to the three acoustic field points constructed in this paper is as high as 50%, which is of great significance in the study of noise reduction in steel–concrete composite girders.

Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI.

Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. To develop and test a suite of open-source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. An open-source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150mm. This phantom was imaged on a CT scanner and a novel split-bore 1.0 T MRI magnet. The CT images provide distortion-free dataset. These data were used to test all modules of the open-source software. All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0±0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5±3.1 to 1.6±0.8mm. Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet.

Directive transportation in smart cities with line connectivity at distinctive points using mode control algorithm

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%.

Research on the artistic form and image expression effect of public sculpture based on intelligent and parametric design

With the modernization of cities, public sculptures are constantly being designed and constructed. The artistic form and image expression effect of sculpture based on intelligent and parametric design needs to be designed and developed to guide and assist the construction of sculpture. This paper applies the NAS architecture search method to explore the field of image expression effect models. Through the end-to-end search of the experiment designed in this paper, the separable convolution lightweight design is used, and the new model AestheticNet is used to predict the image form effect score distribution. Secondly, this paper proposes optimization strategies combining image expression effect theory and convolutional neural network, including improvement of Loss function self-weighted Loss, two-dimensional Attention mechanism – introduction of CBAM, and adaptive pooling layer. Optimization of several aspects, such as adaptive input. Finally, the validation set is compared with other existing image-morphological effect model algorithms, which proves the effectiveness of the customized search scheme. It demonstrates the efficacy of the AestheticNet model compared to other algorithms by validating its prediction of public sculpture image form effect ratings. The artistic form using intelligent and parametric design methodologies may improve. Image expression of sculptures may be enhanced by applying the image form effect model, which should be pervasive. We can use it to intelligently and parametrically guide the design and manufacture of sculptures.